The present invention relates generally to the field of analyzing data for biological or medical tracking or monitoring purposes. More particularly, the present invention relates to analyzing data related to the safety of drugs, vaccines, medical devices, surgical procedures, and other therapeutic interventions for the treatment of disease in humans, animals, or agricultural plants.
Developers of pharmaceutical products, vaccines, medical products and the like are greatly concerned with the potential risk of harm to patients incidental to the use of these therapeutic interventions to treat disease. Agencies which regulate the sale and use of these products generally mandate a certain level of pharmacovigilance activity to measure and report adverse effects associated with the use of these products, in a way that attempts to ascertain whether or not a causal relationship exists between the use of the product and the adverse effect.
The evaluation of drug safety generally begins with in vitro toxicology studies in cultured cells and animals, and then proceeds to randomized, controlled clinical trials. In the present application, references to “drugs” may be understood to apply to vaccines, medical products, and the like as well. In a randomized, controlled clinical trial, a group of patients is divided into two groups. One half of the patients receive the drug under study, and the remainder receives a different “control” treatment, sometimes a placebo. More complex designs are possible as well, and often the assignment is done in a blind fashion so that neither the patient nor the investigator knows which patient has received which treatment until the conclusion of the study. In addition to comparing the efficacy of the drug under study versus the control, the relative safety of the two treatments is compared. This is done by compiling a table of all adverse events which occur. Adverse events are listed by a standardized code, such as the Medical Dictionary for Regulatory Activities (MedDRA) [Brown, 1999]. It should be noted that the complete citation for any referenced publication in provided at the end of this application in a separate section. For each adverse event code, the table includes the number of patients in the drug arm of the study who encountered that adverse event, and the number in the control arm of the study who encountered it as well. The adverse event table generally also includes percentages of the total population of each arm who experienced each type of adverse event. From this table, it is possible to calculate a relative risk for each event type associated with the drug as compared to the control.
Clinical trials must be conducted before a drug is marketed. Regulatory agencies such as the US Food and Drug Administration (FDA) decide to approve drugs based on evaluations of both the safety and the efficacy of the drug, as measured in clinical trials. Once the drug is on the market, a certain amount of additional “post-market” safety evaluation is required. Depending on the degree of risk associated with the drug, this may range from simple surveillance to specific additional studies, as determined by the regulatory agency.
Post-market safety studies are required because it is not possible to fully ascertain the safety of a drug in a controlled clinical trial. Clinical trials generally exclude groups of patients with serious diseases other than the one under study, since such patients would greatly complicate the interpretation of the results. Vulnerable groups such as children and pregnant women may also be excluded. However, all of these kinds of patients may be exposed to the drug once clinical trials are complete and it has been approved and is on the market. In addition, there is the possibility of evolving interaction with new drugs, or food, and lifestyle factors, which may not even exist at the time that the clinical trial is being performed. Drug interactions in general, and interactions between drugs and co-morbid conditions (e.g. worsening of diabetes in a diabetic cancer patient due to an anti-cancer drug), are of great concern in the field of pharmacovigilance.
Systems are in place for post-market pharmacovigilance, that is, the detection of safety signals from marketed drugs. A “safety signal” refers to a concern about an excess of adverse events compared to what would be expected if the occurrence of such events were independent of a drug's use. Regulatory agencies such as the FDA have established programs by which health care providers and patients can report adverse events to drug manufacturers and to the FDA itself. In some cases, these reports may be quite detailed, and may even include experimental re-challenge of a patient with a drug to check for reoccurrence of a non-serious adverse event, such as a rash. In this type of situation, a causal relationship between the drug and the adverse event does not need to rely on statistical techniques. However, in many cases, less detail is available. Reports are tallied into a table of Individual Safety Reports (ISRs), which generally include an approximate date of the event, the drugs that the patient was taking (very often more than one), and codes for the adverse event or events which occurred (also often more than one).
These ISRs are most often compiled into a matrix which indicates the prevalence of many possible drug-event combinations. Note that a complicating factor in using such data for pharmacovigilance is a lack of information about the size of the exposed population. With this type of data, pharmacovigilance methods generally rely on the detection of disproportionality of one drug-event combination compared to the rate at which the event occurs with other drugs. Data analysis methods which have been used with such data include the Multi-Item Gamma Poisson Shrinker (MGPS) [Du Mouchel, 2001; Szarfman 2002], the Proportional Reporting Ratio (PRR) method [Evans, 2001], and the Bayesian Confidence Propagation Neural Network (BCPNN) [Bate, 1998].
Deriving and tracking trends in pharmacovigilance data is an ongoing problem in view of the vast volume of data that needs to be analyzed together with the long timeframes over which much of this data is collected.